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The impact of engineered products is a topic of increasing concern in society. The impact of a product can fall into the categories of economic, environmental, or social impact; the last category is defined as the effect of a product on the daily lives of people. Design teams lack sufficient tools to help improve the impact of products and understand the impact of products at scale in society. This dissertation aims to provide insight and methods for improving the social, environmental, and economic impact of engineered products. The majority of the research focuses on the prediction of product impacts on society, which requires a sociotechnical approach with models that contain aspects of the product and society. This begins with the introduction of an agent-based modeling approach to predict how changes to a design will ultimately impact society. Chapter 3 performs a systematic review of the literature to identify common challenges in product social impact modeling, identifies ways to mitigate the challenges, and provides a general process to create product impact models. Guidance on a general modeling process is essential to enable the widespread use of predictive impact models in engineering design. Chapter 4, provides guidance on creating sociotechnical models using primary survey data and machine learning for impact prediction using a case study of improved cookstoves in Uganda. Chapter 5 presents a method for incorporating environmental impacts, using life cycle assessment and agent-based modeling to properly scale impacts from the functional unit level to the societal level. A limitation of life cycle assessment in the early phases of product design is the difficulty of scaling impacts from the functional unit level to the population level. Using agent-based modeling together with life cycle assessment enables an understanding of the number of functional units required at the population level; allowing for the quantification of the total population-level impact. There are often trade-offs in the social, environmental, and economic sustainability space. To characterize these sustainability trade-offs, Chapter 6 illustrates the modeling of social, environmental, and economic impacts of a product and how to quantify the product sustainability trade-space. Chapter 7, presents work on identifying quantitative factors for selecting engineering global development project locations based on the potential for social impact. Finally, Chapter 8 provides the general contributions of this work, identifies limitations, and provides direction for future work. The research presented in this dissertation is a step toward a future where predictive modeling of the social, environmental, and economic impacts of products is commonplace in engineering design.

Κεντρικό σημείο αναφοράς της διατριβής είναι η σύγχρονη πόλη, στο υπέδαφος της οποίας σώζονται αρχαιολογικά στρώματα. Η ανακάλυψή τους κάτω από τον ενεργό οικιστικό ιστό καθώς και η αρχαιολογική έρευνα που ακολουθεί θεωρούνται από πολλούς αιτία ανάσχεσης της κατασκευαστικής και αναπτυξιακής διαδικασίας. Ωστόσο, εδώ υποστηρίζεται ότι η ύπαρξη αρχαιολογικού υποστρώματος στην πόλη αποτελεί ένα από τα συγκριτικά πλεονεκτήματα της αναπτυξιακής της προοπτικής. Προς τούτο η ερευνητική μεθοδολογία χρησιμοποιεί ποιοτικά και ποσοτικά δεδομένα, ενώ ως μελέτη περίπτωσης επιλέγεται το Σχέδιο Πόλεως των Πατρών. Αρχικά η έρευνα βασίζεται στην αρχειακή και βιβλιογραφική επισκόπηση και στη διεξαγωγή δομημένων συνεντεύξεων με ειδικούς επιστήμονες. Στη συνέχεια, γίνεται χρήση της τεχνολογίας των Γεωγραφικών Συστημάτων Πληροφοριών και της Στατιστικής για τη δημιουργία βάσης δεδομένων, την ψηφιακή επεξεργασία της, την παραγωγή και δημιουργία προγνωστικών μοντέλων και την ανάδειξη της στατιστικής σχέσης της πολεοδομικής με την αρχαιολογική πληροφορία. Από τα αποτελέσματα, αποδεικνύεται ότι είναι δυνατή η κατασκευή μοντέλου πρόβλεψης της πιθανολογούμενης ύπαρξης αρχαίων σε μια πόλη, αλλά και του πιθανολογούμενου βάθους εντοπισμού τους, βασισμένη στην καταγραφή και επεξεργασία της πολεοδομικής και αρχαιολογικής πληροφορίας, που προέρχεται από τις εκσκαφές 5 συνεχόμενων ετών, ακόμα κι αν δεν γνωρίζει κανείς ή δεν λαμβάνει υπόψη τίποτε άλλο από την ιστορία της πόλης αυτής. Χρησιμοποιώντας αρχαιολογικές παραμέτρους σε συνδυασμό με πολεοδομικά δεδομένα είναι δυνατόν να κατασκευαστούν εξειδικευμένα μοντέλα, που μπορούν να αποτυπώσουν τις επιπτώσεις του αρχαιολογικού υποβάθρου μιας πόλης στις τρέχουσες λειτουργίες της και το αντίθετο. Τα αποτελέσματα αυτά μπορούν να χρησιμοποιηθούν τόσο σε επιχειρησιακό επίπεδο, στην άσκηση της αρχαιολογικής έρευνας και της παρακολούθησης της οικοδομικής δραστηριότητας στην πόλη, όσο και ως συμβολή σε μια ευρύτερη διερεύνηση για τη θέση της πολιτιστικής κληρονομιάς στη διαμόρφωση και προβολή της σύγχρονης πόλης. / The dissertation focuses on the contemporary city located on top of archaeological strata. Archaeological remains underneath, as well as their investigation, are considered by many as an obstacle towards the construction and development process. However, it is assumed here that the archaeological remains (below modern cities) consist a comparative advantage towards development. To justify this, qualitative and quantitative research methodology has been employed while the Town Plan of Patras, Greece is used as a case-study. Initially, an archive and literature survey takes place and structured interviews with field experts are conducted. Next, Geographical Information Systems and Statistics are applied for data processing and predictive modeling. Eventually, predictive models of the potential existence of archaeological sites and their expected depth are constructed, based on data from the excavations and the ground disturbance actions of 5 consecutive years. It becomes apparent that the results differ within the built and the unbuilt zones of a town. Using archaeological and urban parameters the impact of the archaeological background, over modern urban functions can be modeled and assessed. Moreover, the outcomes may be used by those involved in making and evaluating policies for the management of cultural heritage within planning.

Netzwerke stellen einen integralen Bestandteil unseres Lebens dar. Eines der wichtigsten Kommunikations-Netzwerke ist das Internet, welches zu starken Veränderungen im Alltag geführt hat. Diese werden in Teilaspekten in der vorliegenden Dissertation untersucht. Insgesamt ist die Dissertation in drei Bereiche unterteilt, welche auf der traditionellen Perspektive der drei Dimensionen von Informationssystemen basieren. Diese Dimensionen umfassen die Technologie, das Management und die Organisation. Im Zentrum der Dissertation steht hierbei die Technologie-Dimension in dessen Rahmen die Struktur und Robustheit des Internets sowie anderer Netzwerke unter Nutzung des mathematisch-methodischen Aspekts der Graphentheorie analysiert werden. Der zweite Teilbereich der vorliegenden Arbeit wechselt die Perspektive hin zum Management. Unter Nutzung von Methoden der prädikativen Modellierung stehen das bessere Verständnis und die Möglichkeit der Vorhersage von Nutzerverhalten im E-Commerce-Kontext im Fokus. Der dritte Bereich umfasst die Organisations-Perspektive aus Sicht der Nutzer. Hierbei werden zwei spezielle Unterbereiche betrachtet. Der erste Unterbereich umfasst Webseiten Sozialer Medien und analysiert wie verschiedene Nutzergruppen diese verwenden. Der zweite Unterbereich befasst sich mit dem Einfluss der weitläufigen Verbreitung von mobilen Endgeräten auf Aspekte des persönlichen und beruflichen Lebens von Individuen. Aufbauend auf diesen drei Dimensionen wurden im Rahmen dieser Dissertation insgesamt 18 Studien durchgeführt, die sich unterschiedlicher methodischer Anwendungen bedienen um wissenschaftliche Erkenntnisse zu den vorgestellten Teilbereichen zu erlangen. / Networks constitute an integral part of our lives. One of the most important communication networks is the Internet which led to large changes in everyday life, which are examined in part in this dissertation. Overall, the present dissertation is subdivided into three areas, which are based on the traditional three dimensions of information systems, comprising perspectives technology, management and organization. At the core of this dissertation is the technological perspective, centered on an analysis of the structure and robustness of the Internet network using the mathematical-methodical aspect of graph theory. The second part of the thesis deals with the management perspective. The focus lies on the understanding and prediction of user behavior in the e-commerce context utilizing methods of predictive modeling. The third area includes the organizational perspective from the point of view of users. Here, two specific sub-areas are selected. The first sub-area revolves around social media websites, with the goal of understanding how sub-groups of users utilize them in different ways. The second area is centered around the aspect of how the propagation of mobile devices influences individuals in their personal and professional environments. Based on these three perspectives, a total of 18 studies were conducted within the scope of this dissertation, using different methodological applications to gain scientific insights with respect to the areas examined.

One group of information systems that have attracted a lot of attention during the past decade are financial information systems, especially systems pertaining to financial markets and electronic trading. Delivering accurate and timely information to traders substantially increases their chances of making better trading decisions.Since the dawn of electronic exchanges the trading community has seen a proliferation of computer-based intelligence within the field, enabled by an exponential growth of processing power and storage capacity due to advancements in computer technology. The financial benefits associated with outperforming the market and gaining leverage over the competition has fueled the research of computational intelligence in financial information systems. This has resulted in a plethora of different techniques.The most prevalent techniques used within algorithmic trading today consist of various machine learning technologies, borrowed from the field of data mining. Neural networks have shown exceptional predictive capabilities time and time again.One recent machine learning technology that has shown great potential is Hierarchical Temporal Memory (HTM). It borrows concepts from neural networks, Bayesian networks and makes use of spatiotemporal clustering techniques to handle noisy inputs and to create invariant representations of patterns discovered in its input stream. In a previous paper [1], an initial study was carried-out where the predictive performance of the HTM technology was investigated within algorithmic trading of financial markets. The study showed promising results, in which the HTM-based algorithm was profitable across bullish-, bearish and horizontal market trends, yielding comparable results to its neural network benchmark. Although, the previous work lacked any attempt to produce near optimal trading models.Evolutionary optimization methods are commonly regarded as superior to alternative methods. The simplest evolutionary optimization technique is the genetic algorithm, which is based on Charles Darwin's evolutionary theory of natural selection and survival of the fittest. The genetic algorithm combines exploration and exploitation in the search for optimal models in the solution space.This paper extends the HTM-based trading algorithm, developed in the previous work, by employing the genetic algorithm as an optimization method. Once again, neural networks are used as the benchmark technology since they are by far the most prevalent modeling technique used for predicting financial markets. Predictive models were trained, validated and tested using feature vectors consisting of technical indicators, derived from the E-mini S&P 500 index futures market.The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models, but both technologies yielded profitable results with above average accuracy. / Program: Magisterutbildning i informatik

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

College completion agendas necessarily presume year-to-year student persistence. Institutional efforts to retain admitted students has emerged for a variety of reasons, some intrinsic and others extrinsic. Some of these reasons include (1) financial exigency as institutions strive to retain tuition-paying students or meet prescribed enrollment and retention criteria currently used in performance funding strategies; (2) reputation enhancement as institutions attempt to ascend annual publications such as the U.S. News & World Report which rely on retention rates as one of several indicators used to measure institutional quality; (3) gaining a perceived advantage in admissions, marketing, and fundraising as persistence rates have, for better or worse, become a de facto measure of quality undergraduate programs; and (4) mission fulfillment as institutions, especially public institutions, are tasked with contributing towards broadly cast social goals such as access to education, economic competitiveness, and community development. Knowledge about forces that impact student attrition is critical to the development of preventative strategies that seek to improve student persistence rates. One such environmental force that has an impact on student persistence is financial aid and a student’s ability to pay for their college education. While research examining the impact of financial aid on student persistence has accumulated over the years, little is known about how the loss of certain types of aid, specifically, state-based merit aid, affects students once they enroll in an institution. The majority of studies about financial aid’s impact on student persistence were conducted prior to the establishment of many state-wide merit scholarship programs. Tinto’s (1975, 1986, 1993) interactional theory of student departure serves as the theoretical framework employed in this study. Tinto (1975) states that entering college students bring with them specific background characteristics and initial commitments that influence the student’s social and academic integration at the institution that, in turn, impact subsequent institutional and goal commitments and, ultimately, persistence. This study intends to examine pre- and post-matriculation data gathered through the admissions and financial aid processes to develop predictive models useful in calculating the probabilities associated with Bright Futures scholarship retention, institutional persistence after losing a Bright Futures scholarship award at the conclusion of a student’s first year of enrollment, and a student’s eligibility to recapture a Bright Futures scholarship award in their third year of enrollment. Data was collected passively from institutional databases on 2,418 students meeting the study criteria for inclusion in the model building process. Findings indicate that the models developed throughout the course of this study hold potential for informing institutional retention initiatives among Bright Futures scholarship award recipients.

Mangroves are heterogeneous, complex ecosystems and detainers o ecological, social and economic relevance. This argument evoked the investigation of the responses of the mangroves to the environmental variations as much in macroscale as in microscale. For this purpose, in general, the research investigated the action of the weather and of the relative sea level over the potential distribution of Avicennia L.; Laguncularia racemosa (L.) Gaertn f.; and Rhizophora mangle L., on the northern and northeastern brazilian coasts, in the time scale 6.000 before present, 2015 and 2050, as well as the phenotypic plasticity of the leaves of the mangroves of Real-Piauí-Fundo estuary complex, due to abiotic and anthropogenic factors that affect the floristic populations on the Southern coast of Sergipe, Brazil. The deductive method and the General Systems Theory have built the theoretical line for the research. On the analysis of the influence of climatic requirements and the influence on the relative sea level for the potential distribution of mangroves, the models generated from this tool of maximum entropy (MAXENT) showed variations of adequability areas in different scenarios. They also highlighted the influence of the annual temperature and the vertical distance to the nearest drainage as important variables on the predictions. From another point of view, to verify the occurrence of variations of shape and size on the leaves of the mangroves on the inferior, middle and superior estuaries of the Real-Piauí-Fundo estuary, in order to examine the phenotypic plasticity of the leaves, and which way the variation patterns are related to the sedimentological, climatic, and salinity of the water factors, samples of the flora, of sediment, of water, and of 450 leaves of A. schauerianna Stapf and Laechm, 600 leaves of Laguncularia racemose (L.) Gaertn f, and 600 for Rhizophora mangle. In the morphometric analysis, 3 anatomic marks and 16 anatomic semimarks were used on the previously digitalized leaf. The analyses revealed that both the leaf lamina and the size of the leaf showed significant variations among the three estuaries (p< 0.01). Then, even in microscale, tendencies of variation in shape and size of the leaves of the mangroves in the three sampling areas occur, submitted to various environmental pressures. With the purpose of evaluating the vulnerability of the mangroves of estuary complex, among anthropogenic tensors and the possible relation of these tensors with the phenotypic plasticity of the leaf, the methodology was created from systematic observations; from the fixation of attributes of anthropic tension; from the setting of weights for the group of anthropogenic tensors acting of mangroves; from the establishment of levels of vulnerability; and from the analyses of linear regression among shape, size and anthropic tensors. The sector with higher vulnerability is the superior estuary. But, all the areas are submitted to the risks of human actions. The results also showed that both the shape and the size of the leaves of A. schauerianna and R. mangle had significant values (p< 0.01), in contrast to L. racemosa. When concluding and making suggestions, the thesis indicates it´s possible to recreate the ecological history of the environments, to understand the phenomena of the present, both from the predictive modeling, and from the morphogeometric evaluations, and subsidize actions of the mangrove conservations, in order to avoid future losses of the mangrove flora. / Os manguezais são ecossistemas heterogêneos, complexos e detentores de relevância ecológica, social e econômica. Esta assertiva suscitou a investigação das respostas dos mangues às variações ambientais tanto em macroescala, como em microescala. Para tanto, de forma geral, a pesquisa investigou a atuação do clima e do nível relativo do mar sobre a distribuição potencial de Avicennia L.; Laguncularia racemosa (L.) Gaertn.f.; e Rhizophora mangle L., no litoral das regiões norte e nordeste do Brasil, na escala temporal 6.000 antes do presente (A.P.), 2015 e 2050, bem como a plasticidade fenotípica das folhas dos mangues do complexo estuarino Real-Piauí-Fundo, decorrentes de fatores abióticos e antropogênicos que afetam populações florísticas no litoral sul de Sergipe, Brasil. O método dedutivo e a Teoria Geral dos Sistemas constituíram o fio condutor teórico para construção da pesquisa. Na análise da influência de requerimentos climáticos e de influência no nível relativo do mar para distribuição potencial dos mangues, os modelos gerados a partir da ferramenta de Máxima Entropia (MAXENT) mostraram variações de áreas de adequabilidade nos distintos cenários. Eles também destacaram a influência da temperatura anual e da distância vertical à drenagem mais próxima como variáveis de importância nas predições. Por outro ângulo, para verificar a ocorrência de variações de forma e tamanho das folhas de mangues nos estuários inferior, médio e superior do complexo estuarino Real-Piauí-Fundo, a fim de examinar a plasticidade fenotípica das folhas e de que maneira os padrões de variação estão relacionados aos fatores sedimentológicos, climáticos e de salinidade da água, foram coletadas e analisadas amostras da flora, de sedimento, água e de 450 folhas para A. schaueriana Stapf e Laechm, 600 para Laguncularia racemosa (L.) Gaertn.f.e e 600 para Rhizophora mangle L. Na análise morfométrica foram utilizados 3 marcos e 16 semimarcos anatômicos ao longo da folha previamente digitalizada. As análises revelaram que tanto a forma da lâmina foliar, como o tamanho das folhas apresentaram variações significativas entre os três estuários (p<0.01). Logo, mesmo em microescala, ocorrem tendências de variação na forma e tamanho das folhas de mangues nas três áreas de amostragem, submetidas às diferentes pressões ambientais. Quanto à forma e tamanho, não existiu efeito de alometria (p< 0.01). Com o propósito de avaliar a vulnerabilidade dos mangues do complexo estuarino, em meio aos tensores antropogênicos e a possível relação destes com a plasticidade fenotípica foliar, a metodologia foi construída a partir de observações sistemáticas; da fixação de atributos de tensão antrópica; da determinação de pesos para o conjunto de tensores antropogênicos atuantes sobre os mangues; do estabelecimento de níveis de vulnerabilidade; e das análises de regressão linear entre forma, tamanho e tensores antrópicos. O setor de maior vulnerabilidade é o estuário superior. Porém, todas as áreas estão submetidas aos riscos das ações humanas. Os resultados apontaram também que tanto a forma como o tamanho das folhas de A. schaueriana e R. mangle possuíram valores significativos (p<0.01), ao contrário da L. racemosa. Ao concluir e fazer sugestões, a tese indica que é possível reconstruir a história ecológica dos ambientes, entender os fenômenos do presente, tanto a partir da modelagem preditiva, como das avaliações morfogeométricas, e subsidiar ações de conservação dos mangues, a fim de evitar futuras perdas da flora de manguezal.

The paper describes predictive modelling of biomarker data stemming from patients suffering from multiple sclerosis. Improvements of multivariate analyses of the data are investigated with the goal of increasing the capability to assign samples to correct subgroups from the data alone. The effects of different preceding scalings of the data are investigated and combinations of multivariate modelling methods and variable selection methods are evaluated. Attempts at merging the predictive capabilities of the method combinations through voting-procedures are made. A technique for improving the result of PLS-modelling, called bagging, is evaluated. The best methods of multivariate analysis of the ones tried are found to be Partial least squares (PLS) and Support vector machines (SVM). It is concluded that the scaling have little effect on the prediction performance for most methods. The method combinations have interesting properties – the default variable selections of the multivariate methods are not always the best. Bagging improves performance, but at a high cost. No reasons for drastically changing the work flows of the biomarker data analysis are found, but slight improvements are possible. Further research is needed.

The aim is to develop a new decision-making model for credit-loans. The model will be specific for credit applicants of the OKQ8 bank, becauseit is based on data of earlier applicants of credit from the client (the bank). The final model is, in effect, functional enough to use informationabout a new applicant as input, and predict the outcome to either the good risk group or the bad risk group based on the applicant’s properties.The prediction may then lay the foundation for the decision to grant or deny credit loan. Because of the skewed distribution in the response variable, different sampling techniques are evaluated. These include oversampling with SMOTE, random undersampling and pure oversampling in the form of scalar weighting of the minority class. It is shown that the predictivequality of a classifier is affected by the distribution of the response, and that the oversampled information is not too redundant. Three classification techniques are evaluated. Our results suggest that a multi-layer neural network with 18 neurons in a hidden layer, equippedwith an ensemble technique called boosting, gives the best predictive power. The most successful model is based on a feed forward structure andtrained with a variant of back-propagation using conjugate-gradient optimization. Two other models with a good prediction quality are developed using logistic regression and a decision tree classifier, but they do not reach thelevel of the network. However, the results of these models are used to answer the question regarding which customer properties are importantwhen determining credit risk. Two examples of important customer properties are income and the number of earlier credit reports of the applicant. Finally, we use the best classification model to predict the outcome of a set of applicants declined by the existent filter. The results show that thenetwork model accepts over 60 % of the applicants who had previously been denied credit. This may indicate that the client’s suspicionsregarding that the existing model is too restrictive, in fact are true.